High-Accuracy Quantitative Nuclear Magnetic Resonance Using Improved Solvent Suppression Schemes
Bibliographic record
Abstract
Quantitative nuclear magnetic resonance (qNMR) has contributed to reliable and accurate measurements of organic compounds enabling quantitation even when no standards of the specific compounds are available. Such high-accuracy determinations are critical across the field of analytical chemistry, with the advances in qNMR being of utmost importance in the production of reference standards for a range of organic compounds. The ability to perform these accurate measurements in the presence of natural isotopic abundance solvents is important for increasing throughput and expanding the number of applications that benefit. In this work, we have assessed several pulse sequences for solvent suppression. The limitations of NMR acquisitions in the presence of large solvent signals and solvent suppression such as limited dynamic range, losses due to relaxation and proximity to the solvent peak where quantitation starts failing were studied in depth and discussed. We have shown that binomial-like sequences produce the most robust and reliable results in the majority of scenarios and propose alternative sequences using modern pulses that produce satisfactory results in situations where the most accurate sequences are not applicable. We present the development and use of binomial-like pulses in an inversion-recovery sequence that allows T1 measurement in experiments without deuterated solvent (no-D NMR) to enable the use of correct repetition times for high-accuracy measurements under these conditions. Although the binomial-like sequences present the limitation of having secondary suppression notches, there is enough flexibility to adjust the position of those notches. Finally, we present a full measurement uncertainty budget estimation including all uncertainty allowances that are relevant when solvent suppression is used.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".